Abstract
Computational stylistics focuses on description and quantifiable expression of linguistic styles of written documents that enables author characterisation, comparison, and attribution. It is a case when observation of subtle relationships in data sets is required, with domain knowledge uncertain. Therefore, techniques from the artificial intelligence area, such as Dominance-based Rough Set Approach (DRSA), are well suited to handle the problem. DRSA enables construction of a rule-based classifier consisting of decision rules, selection of which can greatly influence classification accuracy. The paper presents research on application of DRSA classifier in author recognition for literary texts, with considerations on the classifier performance based on an analysis of relative reducts, such subsets of features that maintain classification properties.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burrows, J.: Textual analysis. In: Schreibman, S., Siemens, R., Unsworth, J. (eds.) A companion to digital humanities. Blackwell, Oxford (2004)
Craig, H.: Stylistic analysis and authorship studies. In: Schreibman, S., Siemens, R., Unsworth, J. (eds.) A companion to digital humanities, Blackwell, Oxford (2004)
Greco, S., Matarazzo, B., Slowinski, R.: Dominance-based rough set approach as a proper way of handling graduality in rough set theory. Transactions on Rough Sets 7, 36–52 (2007)
Pawlak, Z.: Rough sets and intelligent data analysis. Information Sciences 147, 1–12 (2002)
Peng, R., Hengartner, H.: Quantitative analysis of literary styles. The American Statistician 56(3), 15–38 (2002)
Shen, Q.: Rough feature selection for intelligent classifiers. Transactions on Rough Sets 7, 244–255 (2006)
Słowiński, R., Greco, S., Matarazzo, B.: Dominance-based rough set approach to reasoning about ordinal data. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 5–11. Springer, Heidelberg (2007)
Stanczyk, U.: Dominance-based rough set approach employed in search of authorial invariants. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol. 57, pp. 293–301. Springer, Heidelberg (2009)
Stańczyk, U.: DRSA decision algorithm analysis in stylometric processing of literary texts. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 600–609. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stańczyk, U. (2011). Reduct-Based Analysis of Decision Algorithms: Application in Computational Stylistics. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-21222-2_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21221-5
Online ISBN: 978-3-642-21222-2
eBook Packages: Computer ScienceComputer Science (R0)